Wavelet transform and fuzzy ARTMAP-based pattern recognition for fast gas identification using a micro-hotplate gas sensor

It is shown that a single thermally-modulated tin oxide-based resistive microsensor can discriminate between two different pollutant gases (CO and NO2) and their mixtures. The method employs a novel feature-extraction and pattern classification method, which is based on a 1-D discrete wavelet transform and a Fuzzy adaptive resonant theory map (ARTMAP) neural network. The wavelet technique is more effective than FFT in terms of data compression and is highly tolerant to the presence of additive noise and drift in the sensor responses. Furthermore, Fuzzy ARTMAP networks lead to a 100% success rate in gas recognition in just two training epochs, which is significantly lower than the number of epochs required to train the back-propagation network.